ICA1 affects APP processing through the PICK1‐PKCα signaling pathway

Abstract Aims Islet cell autoantigen 1 (ICA1) is involved in autoimmune diseases and may affect synaptic plasticity as a neurotransmitter. Databases related to Alzheimer's disease (AD) have shown decreased ICA1 expression in patients with AD. However, the role of ICA1 in AD remains unclear. Here, we report that ICA1 expression is decreased in the brains of patients with AD and an AD mouse model. Results The ICA1 increased the expression of amyloid precursor protein (APP), disintegrin and metalloprotease 10 (ADAM10), and disintegrin and metalloprotease 17 (ADAM17), but did not affect protein half‐life or mRNA levels. Transcriptome sequencing analysis showed that ICA1 regulates the G protein‐coupled receptor signaling pathway. The overexpression of ICA1 increased PKCα protein levels and phosphorylation. Conclusion Our results demonstrated that ICA1 shifts APP processing to non‐amyloid pathways by regulating the PICK1‐PKCα signaling pathway. Thus, this study suggests that ICA1 is a novel target for the treatment of AD.


| INTRODUC TI ON
Alzheimer's disease (AD) is the most common neurodegenerative disorder that causes dementia. 14][5] When there are no interfering factors, the majority of APPs undergo processing by α-secretase at the Leu17 position within the Aβ domain.This process results in the formation of a large soluble fragment known as sAPPα and a membrane-bound C-terminal fragment composed of 83 amino acids, referred to as CTFα or C83.Subsequently, γ-secretase cleaves C83, leading to the production of P3 fragments and CTFγ.This process is characterized as a non-amyloidogenic pathway.In AD, the amyloidogenic process begins with the cleavage of APP by β-site APP cleaving enzyme 1 (BACE1).This cleavage at the Asp1 location results in the formation of a C99 fragment and sAPPβ, following which γsecretase cleaves C99, leading to the formation of Aβ. [5][6][7] APP can also be cleaved by BACE1 at the Glu11 position, resulting in the formation of a C89 fragment and sAPPβ.Alternatively, it can be cleaved by BACE2 at the Phe20 position, leading to the production of a C80 fragment and sAPPθ.Subsequently, γ-secretase further cleaves C89 and C80, generating truncated Aβ and P3θ, respectively. 8,9The imbalance between production and clearance causes Aβ to deposit and form plaques that promote intracellular neurofibrillary tangles, oxidative stress, neuroinflammation, neuronal death, and synaptic loss. 10,11Although many studies have explored APP processing and the pathogenesis of AD, the mechanism underlying APP processing is still being investigated, and there is currently no effective treatment for AD.
To find a new target, we searched AD-related databases.
Transcriptome sequencing analysis of patients with AD has shown that Islet cell autoantigen 1 (ICA1) is significantly reduced in the brains of patients with AD, 12 suggesting that ICA1 may be involved in the development and progression of AD, but the exact role is unclear.
Islet cell autoantigen 1 (ICA1), also known as ICA69 (Islet cell antigen p69), is located on chromosome 7p21.3and consists of the Bin/ amphiphysin/Rvs (BAR) and ICAC domains.4][15][16] The C-terminal 257-480 amino acids form the ICAC domain, and the amino acid sequences are highly evolutionarily conserved. 17e ICA1 protein was first identified as a cross-reacting protein in cloned rat β-islet tumor cell extracts or isolated from BB rat islets using rat anti-bovine serum albumin antiserum. 18Thus, ICA1 is thought to be an autoantigen that causes Type I diabetes (IDDM). 19 functions as an autoimmune target antigen in primary Sjogren's syndrome, rheumatoid arthritis, and other autoimmune diseases. 20,21ICA1 is widely expressed throughout the body, primarily in the pancreas, muscles, digestive tract, and brain.Immunoelectron microscopy has shown ICA1 subcellular localization in the endoplasmic reticulum, Golgi complex, and vesicles, suggesting the role of this neuroendocrine molecule in cellular protein transport and processing. 22,23e Rab GTPases are a large family of GTPases that control membrane trafficking by recruiting effector proteins, such as sorting adaptors, tethering factors, kinases, phosphatases, and motors, mediate the various downstream functions of Rab GTPases, including membrane identity, vesicle budding, uncoating, motility, and fusion. 24 has been shown that Rab2 binds to ICA1 in a GTP-dependent fashion, recruits it to membranes in insulinoma INS-1 cells, and regulates the transport of coat protein complex I(COPI) vesicles between the endoplasmic reticulum and the Golgi complex. 25 the brain, more than three-fourths of ICA1 and proteins interacting with C kinase 1(PICK1) bind to each other through the BAR domain to form heterogenic complexes. 15PICK1 is an adaptor protein that attaches to and arranges the subcellular positioning of a variety of membrane proteins and has an interactive relationship with protein kinase Cα (PKCα). 26PICK1 has a PDZ (PSD-95/Dlg/ ZO1) domain that engages with the C terminal of AMPA receptors.
Additionally, the BAR domain of PICK1 attaches to the membrane, facilitating the transport of AMPA receptors. 27However, ICA1 binds to PICK1, affects AMPA receptor recruitment, and influences synaptic plasticity. 15,28re, our observations indicated that in the APP23/PS45 mouse model, the levels of ICA1 were lower than those in the wild-type mice of the same age.Furthermore, ICA1 affects APP processing through the PICK1-PKCα signaling pathway.Finally, we treated cells overexpressing ICA1 with the PKCα inhibitor Go 6983, which rescued the increased protein expression.Our findings show that ICA1 is reduced in AD and affects APP processing through the PICK1-PKCα signaling pathway.

| Transcriptome sequencing
Total RNA was extracted using Trizol reagent (Thermo Fisher, 15,596,018) following the manufacturer's procedure.The total RNA quantity and purity were analyzed with the Bioanalyzer 2100 and RNA 6000 Nano LabChip Kit (Agilent, 5067-1511).High-quality RNA samples with an RIN number >7.0 were used to construct a sequencing library.After total RNA was extracted, mRNA was purified from the total RNA (5 ug) using Dynabeads Oligo (dT) (Thermo Fisher) with two rounds of purification.Following purification, the mRNA was fragmented into short fragments using divalent cations under elevated temperature (Magnesium RNA Fragmentation Module (NEB, cat.e6150) at 94°C 5-7 min).Then the cleaved RNA fragments ).We used the human ensembl database version 107 genome for mapping. 29We aligned the reads of all samples to the reference genome using HISAT2 (https:// daehw ankim lab.github.io/ hisat2/ , version:hisat2-2.2.1) package, which initially remove a portion of the reads based on quality information accompanying each read and then maps the reads to the reference genome.
The mapped reads of each sample were assembled using StringTie (http:// ccb.jhu.edu/ softw are/ strin gtie/ , version:stringtie-2.1.6)with default parameters.Gene differential expression analysis was performed by DESeq2 software (version: 1.22.2) between two different groups.The genes with the parameter of false discovery rate (FDR) below 0.05 and an absolute fold change of ≥2 were considered differentially expressed genes.Differentially expressed genes were then subjected to enrichment analysis of GO functions and KEGG pathways in OmicStudio. 30GO terms meeting this condition with p < 0.05 were defined as significantly enriched GO terms in DEGs.
We performed gene set enrichment analysis using software GSEA (v4.1.0)and MSigDB to identify whether a set of genes in specific GO terms, KEGG pathways, DO terms (for Homo sapiens), and Reactome (for a few model animals) shows significant differences in two groups.Only |NES|>1, NOM p-val <0.05, FDR q-val <0.25 were considered to be different in two groups. 31,32NES is the normalized enrichment score after correction.NOM p-val is p-value, a statistical analysis of enrichment scores used to indicate the credibility of enrichment results.

| Statistical analysis
All data were shown as mean ± SEM, and all results were analyzed using Shapiro-Wilk test to assess data distribution.Two-tailed Student's t-test, two-tailed Welch's t-test, one-way ANOVA test, and two-way ANOVA test were used to analyze parametric data appropriately.Non-parametric data were assessed by Mann-Whitney test.

| ICA1 was reduced in the brains of AD
To verify the decreased expression of ICA1 in AD, we searched for ICA1 in the Alzdata database and performed the differential expression analysis. 33We found that the mRNA levels of ICA1 in various parts of the brain in patients with AD were reduced compared to those in normal individuals (Figure 1A), suggesting that ICA1 may play a role in the pathological process of AD.To further investigate its role in AD, we extracted cortical and hippocampal tissue proteins from 3-month-old APP23/PS45 and C57 mice (APP23/PS45 n = 6, C57 n = 6) and detected the expression of ICA1 by western blotting (WB).The expression of ICA1 was also reduced in the cortex and hippocampus of APP23/PS45 mice (p < 0.05, Figure 1B-D).These data suggest that ICA1 expression is reduced in the brain in AD.
Since Aβ, which is generated through APP processing, is the most critical factor in the development of AD.We then first investigated whether ICA1 affects APP processing.
These data indicate that ICA1 overexpression affects APP processing and increases α-secretase cleavage of APP.
These data further suggest that ICA1 alters APP processing.

| ICA1 did not affect the degradation or mRNA level of APP, ADAM10, and ADAM17
To explore whether the effect of ICA1 on APP processing was caused by impaired degradation, 20E2 cells overexpressing ICA1 were treated with cycloheximide (CHX), and the protein levels of APP, ADAM10, and ADAM17 were analyzed.Quantification revealed that ICA1 had no effect on the catabolism of APP, ADAM10, and ADAM17 (n = 4 for APP, n = 3 for ADAM10, n = 3 for ADAM17, p > 0.05 for all, Figure 4A-F).These data indicated that ICA1 does not affect APP, ADAM10, or ADAM17 degradation.We then extracted RNA from HEK 293 cells overexpressing ICA1 to determine whether ICA1 enhanced synthesis using qPCR to detect the mRNA levels of APP, ADAM10, and ADAM17.The results showed that ICA1 overexpression did not affect the transcription of APP, ADAM10, or ADAM17 (n = 3, p > 0.05 for all except p < 0.01 for ICA1, Figure 4G).

| Transcriptome sequencing analysis of ICA1 knockdown in 20E2
To explore the specific mechanism of ICA1 affecting APP processing, we performed transcriptome sequencing analysis between the knockdown and negative control groups (n = 3).Differential expression analysis identified 581 genes, of which 283 genes were significantly up-regulated and 298 genes were significantly down-regulated (Figure 5A,B, Table S1).Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms (Figure 5D,E, Table S1) were analyzed.In GO terms, "signal transdution," "regulation of transcription by RNA polymerase II," "regulation of transcription, DNA-templated," "ion transport," "membrane," "integral component of membrane," "cytoplasm," "nucleus," "plasma membrane," "protein binding," and "G protein-coupled receptor signaling pathway" were enriched.KEGG pathway enrichment analysis revealed that these DEGs were mainly enriched in the apoptosis, rap1 signaling pathway, calcium signaling pathway, ras signaling pathway, neuroactive ligand-receptor interaction, MAPK signaling pathway, metabolism pathway, and chemokine signaling pathway.Consistently, gene set enrichment analysis (GSEA) showed significant enrichment in "liganded gq/11 activating gpcrs act as gefs for gq/11" (Figure 5C).The gq/11 is upstream of PKC.It activates PKC by increasing PLC activity to produce the intracellular messengers inositol triphosphate (IP3) and diacylglycerol (DAG).

| ICA1 affects APP processing through PICK1-PKCα signaling pathway
In previous transcriptome sequencing results, we found that ICA1 knockdown affected the PKC signaling pathway.It has been shown that ICA1 indirectly binds to PKCα after binding to PICK1.We PKCα, a subtype of G protein-coupled receptor, is a family of phospholipid-dependent serine/threonine kinases with either a canonical or dual binding mode with PICK1. 34PKC activation in neurons increases the Ser880 phosphorylation of the GluR2 subunit and recruits PICK1 to excitatory synapses.PKC stimulation in neurons results in the rapid internalization of surface GluR2 subunits.Therefore, PKC modulates the surface expression of AMPA receptors during synaptic plasticity. 35It has been shown that ICA1 regulates the trafficking of the PKCα-PICK1 complex to the plasma membrane. 15,28Many studies have shown that PKCα cotranslocates with ADAM10 to the cell membrane and modulates were reverse-transcribed to create the cDNA by SuperScript™ II Reverse Transcriptase (Invitrogen, cat.1,896,649), which were next used to synthesize U-labeled second-stranded DNAs with E. coli DNA polymerase I (NEB, cat.m0209),RNase H (NEB, cat.m0297) and dUTP Solution (Thermo Fisher, cat.R0133).An A-base was then added to the blunt ends of each strand, preparing them for ligation to the indexed adapters.Each adapter contained a T-base overhang for ligating the adapter to the A-tailed fragmented DNA.Dual-index adapters were ligated to the fragments, and size selection was performed with AMPureXP beads.After the heat-labile UDG enzyme (NEB, cat.m0280) treatment of the U-labeled second-stranded DNAs, the ligated products were amplified with PCR by the following conditions: initial denaturation at 95°C for 3 min; 8 cycles of denaturation at 98°C for 15 s; annealing at 60°C for 15 s; and extension at 72°C for 30 s; and then final extension at 72°C for 5 min.The average insert size for the final cDNA libraries was 300 ± 50 bp.At last, we performed the 2 × 150 bp paired-end sequencing (PE150) on an Illumina Novaseq™ 6000 (LC-Bio Technology Co., Ltd.) following the vendor's recommended protocol.All data were analyzed by R (version: 3.6

F
I G U R E 6 ICA1 affects APP processing through PICK1-PKCα signaling pathway.(A) Western blotting showed that ICA1 affects APP processing via the PICK1-PKCα signaling pathway.(B) Quantification of the relative protein level of ICA1 (n = 3) in the ICA1 overexpression group compared to the vector group in 20E2, *p < 0.05 for both.The data conformed to a normal distribution by Shapiro-Wilk test.p Value was determined by a two-tailed Student's t-test.(C) Quantification of the relative protein level of PICK1 (n = 3) in the ICA1 overexpression group compared with that in the vector group in 20E2 cells, p > 0.05.The data conformed to a normal distribution by Shapiro-Wilk test.p Value was determined by a one-way ANOVA test.(D) Quantification of the relative protein level of PKCα (n = 3) after ICA1 overexpression and PKCα inhibition, *p < 0.05.(E) Quantification of the relative protein level of p-PKCα (n = 3) after ICA1 overexpression and PKCα inhibition, **p < 0.01, ***p < 0.001.(F) Quantification of the relative protein level of C83 (n = 3) after ICA1 overexpression and PKCα inhibition, *p < 0.05, **p < 0.01, ***p < 0.001.(G) Quantification of the relative protein level of APP (n = 3) after ICA1 overexpression and PKCα inhibition, *p < 0.05, **p < 0.01, ***p < 0.001.(H) Quantification of the relative protein level of ADAM10 (n = 3) after ICA1 overexpression and PKCα inhibition, *p < 0.05, ***p < 0.001.(I) Quantification of the relative protein level of ADAM17 (n = 3) after ICA1 overexpression and PKCα inhibition, *p < 0.05, **p < 0.01.The data for each group conformed to a normal distribution by Shapiro-Wilk test.p Value was determined by a one-way ANOVA test.